This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

published:29 Mar 2016

views:8722

A speaker-dependent speech recognition system using a back-propagated neural network. MFCC feature extraction method used.
System designed to recognise words 1-8.

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features
AspireResearchFoundationThe InternationalConference on DataEngineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
All Accepted & Registered Papers will be published in AISCSeries of Springerhttp://www.springer.com/series/11156
** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **
Aspire Research Foundation
http://aspire-research.org/
ICDECT 2016
http://icdect.com/
Aspire Team
http://aspire-research.org/innerhome.jsp?page=our_team.html
Advisory Commitee
http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html
-- -------------------------------------------------------------------------------------------------------
Regards
Ganesh Khedkar
Director
Aspire Research Foundation
Email:ganesh@aspire-research.org
http://aspire-research.org/

published:19 Jun 2016

views:560

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is found with the project. ConfusionMatrix is also shown.

Speech recognition

Speech recognition (SR) is the inter-disciplinary sub-field of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as Smart Technologies and robotics. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT).

Some SR systems use "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent".

Computer science

Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.

Matlab code for MFCC DCT extraction and sound classification

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

MFCC Matlab Speech Recognition

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

Speaker Recognition Using MFCC and Vector Quantization

Performance Analysis of LPC and MFCC Features in Voice Conversion using Articial Neural Networks

Performance Analysis of LPC and MFCC Features in Voice Conversion using Articial Neural Networks

Performance Analysis of LPC and MFCC Features in Voice Conversion using Articial Neural Networks

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features
AspireResearchFoundationThe InternationalConference on DataEngineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
All Accepted & Registered Papers will be published in AISCSeries of Springerhttp://www.springer.com/series/11156
** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **
Aspire Research Foundation
http://aspire-research.org/
ICDECT 2016
http://icdect.com/
Aspire Team
http://aspire-research.org/innerhome.jsp?page=our_team.html
Advisory Commitee
http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html
-- -------------------------------------------------------------------------------------------------------
Regards
Ganesh Khedkar
Director
Aspire Research Foundation
Email:ganesh@aspire-research.org
http://aspire-research.org/

6:03

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is found with the project. ConfusionMatrix is also shown.

4:05

Speech Recognition Matlab Code MFCC

Speech Recognition Matlab Code MFCC

Speech Recognition Matlab Code MFCC

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sound files.
All sound files are recorded in real time.
KNN classifier is used for classification of sound file based on the trained sound file
Code is having 100% recognition rate.
This Matlab code automatically decodes DTMF tone file input by user.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
Explanation of the codes are provided over phone
This code can be edited in any way to suite any application of speech recognition, examples being home automation, speech to text convertor, voice based search engine etc
For all your Matlab needs, Matlabz.com

Matlab code for MFCC DCT extraction and sound classification

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

Lecture 10.2 Source Signal Feature Extraction

MFCC Matlab Speech Recognition

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

published: 13 Apr 2017

Speaker Recognition Using MFCC and Vector Quantization

Performance Analysis of LPC and MFCC Features in Voice Conversion using Articial Neural Networks

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (...

published: 19 Jun 2016

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is f...

published: 16 Jul 2017

Speech Recognition Matlab Code MFCC

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sound files.
All sound files are recorded in real time.
KNN classifier is used for classification of sound file based on the trained sound file
Code is having 100% recognition rate.
This Matlab code automatically decodes DTMF tone file input by user.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
Explanation of the codes are provided over phone
This code can be edited in any way to suite any applica...

Matlab code for MFCC DCT extraction and sound classification

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input...

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

MFCC Matlab Speech Recognition

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and ...

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features
AspireResearchFoundationThe InternationalConference on DataEngineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
All Accepted & Registered Papers will be published in AISCSeries of Springerhttp://www.springer.com/series/11156
** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **
Aspire Research Foundation
http://aspire-research.org/
ICDECT 2016
http://icdect.com/
Aspire Team
http://aspire-research.org/innerhome.jsp?page=our_team.html
Advisory Commitee
http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html
-- -------------------------------------------------------------------------------------------------------
Regards
Ganesh Khedkar
Director
Aspire Research Foundation
Email:ganesh@aspire-research.org
http://aspire-research.org/

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features
AspireResearchFoundationThe InternationalConference on DataEngineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
All Accepted & Registered Papers will be published in AISCSeries of Springerhttp://www.springer.com/series/11156
** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **
Aspire Research Foundation
http://aspire-research.org/
ICDECT 2016
http://icdect.com/
Aspire Team
http://aspire-research.org/innerhome.jsp?page=our_team.html
Advisory Commitee
http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html
-- -------------------------------------------------------------------------------------------------------
Regards
Ganesh Khedkar
Director
Aspire Research Foundation
Email:ganesh@aspire-research.org
http://aspire-research.org/

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is found with the project. ConfusionMatrix is also shown.

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is found with the project. ConfusionMatrix is also shown.

Speech Recognition Matlab Code MFCC

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sou...

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sound files.
All sound files are recorded in real time.
KNN classifier is used for classification of sound file based on the trained sound file
Code is having 100% recognition rate.
This Matlab code automatically decodes DTMF tone file input by user.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
Explanation of the codes are provided over phone
This code can be edited in any way to suite any application of speech recognition, examples being home automation, speech to text convertor, voice based search engine etc
For all your Matlab needs, Matlabz.com

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sound files.
All sound files are recorded in real time.
KNN classifier is used for classification of sound file based on the trained sound file
Code is having 100% recognition rate.
This Matlab code automatically decodes DTMF tone file input by user.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
Explanation of the codes are provided over phone
This code can be edited in any way to suite any application of speech recognition, examples being home automation, speech to text convertor, voice based search engine etc
For all your Matlab needs, Matlabz.com

Matlab code for MFCC DCT extraction and sound classification

This code classifies input sound file using the MFCC + DCT parameters. MFCC + DCT is extracted from the input file. KNN classifier is used to classify the input sound file based on the extracted parameters. KNN Classifier is trained with a set of sound files in different categories. During training also MFCC + DCT parameters of each of the training sound file is captured.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
All codes are neatly commented, line by line, Explanation of the codes are provided over phone or skype.
Satisfaction Guaranteed!

MFCC Matlab Speech Recognition

This code extracts MFCC features from training and testing samples, uses vector quantization to find the minimum distance between MFCC features of training and testing samples, and thus find the testing sample word.
First there is a training phase, and then there is a testing phase
100% recognition rate as you can see in the video
This code will not be given for free!
If you want to BUY this code, drop an email to matlabzindia@gmail.com

Performance Analysis of LPC and MFCC Features in Voice Conversion using Articial Neural Networks

Shashidhar G. Koolagudi, KavyaVishwanath B, Akshatha. M, and YarlagaddaV S Murthy
National Institute of Technology Karnataka, Surathkal, Karnataka - 575 025. koolagudi@yahoo.com, kavyabvishwanath25@gmail.com, akshatham@gmail.com, urvishnu@gmail.com home page: texttthttp://cse.nitk.ac.in A
ABSTRACT
VoiceConversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source-target re- lationship from a number of utterances from source and the target. There are many applications which may benet from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this pa-per, Analysis on the performance of ANN based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coecients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features
AspireResearchFoundationThe InternationalConference on DataEngineering and Communication Technology-ICDECT will be held during March 10 -11, 2016 at LAVASA, Pune. ICDECT, is to bring together innovative academics and industrial experts in the field of Computer Science and Electronics Engineering to a common forum. ICDECT will provide an Excellent international forum for sharing knowledge and results in Computer Science and Electronics Engineering. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet the share cutting-edge development in the field. The primary goal of the conference is to promote research and developmental activities in Computer Science and Electronics Engineering. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Computer Science and Electronics Engineering and related areas.
All Accepted & Registered Papers will be published in AISCSeries of Springerhttp://www.springer.com/series/11156
** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **
Aspire Research Foundation
http://aspire-research.org/
ICDECT 2016
http://icdect.com/
Aspire Team
http://aspire-research.org/innerhome.jsp?page=our_team.html
Advisory Commitee
http://aspire-research.org/innerhome.jsp?page=advisory_commitee.html
-- -------------------------------------------------------------------------------------------------------
Regards
Ganesh Khedkar
Director
Aspire Research Foundation
Email:ganesh@aspire-research.org
http://aspire-research.org/

6:03

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

Speaker Independent Isolated Word Recogntition System using mfcc and DWT

This Video shows MATLAB implementation of Speaker Independent Isolated Word Recogntition System using Mel Frequency Cepstrum Coefficient (mfcc) and DynamicTime Wrapping (DWT).
mfcc are extracted as Features and DWT is used as Matching Algorithm in this project.
The Database used in this project, was downloaded from Internet a long time ago, so I don't remember the actual source. However, in this project, 11 words (1 to 9, 0 and O) are uttered by 5 different speakers, 55 audio files are used in training and same number of audio files are used for testing purpose also. There are two folders. In 'train' named folder, 55 audio files (11x5) are stored and 55 different audio files are also stored in 'test' named folder.
80% Accuracy, 20% Error rate, 96% Specificity and 100% Sensitivity is found with the project. ConfusionMatrix is also shown.

4:05

Speech Recognition Matlab Code MFCC

This is the Matlab code for automatic recognition of speech. Any number of words can be tr...

Speech Recognition Matlab Code MFCC

This is the Matlab code for automatic recognition of speech. Any number of words can be trained.
MFCC feature alone is used for extracting the features of sound files.
All sound files are recorded in real time.
KNN classifier is used for classification of sound file based on the trained sound file
Code is having 100% recognition rate.
This Matlab code automatically decodes DTMF tone file input by user.
If you want to 'BUY' this code, please drop an email to matlabzindia@gmail.com
If you need matlab coding help, please visit matlabz.com and register a request
We create matlab codes from scratch as per customer request
You need to make the payment only after you see the output
Explanation of the codes are provided over phone
This code can be edited in any way to suite any application of speech recognition, examples being home automation, speech to text convertor, voice based search engine etc
For all your Matlab needs, Matlabz.com

My jarvis use MFCC speech recognition.... part 1

Speech recognition

Speech recognition (SR) is the inter-disciplinary sub-field of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as Smart Technologies and robotics. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT).

Some SR systems use "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent".

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MFC

Sliding out of reverse into drive, thisWe will be turning right, then straightOff in the sunset she'll rideShe can remember a time deniedStood by the side of the road, spilled like wine nowShe's out on her own and I'm highThere's no leaving hereAsk- I'm an earShe's disappearedThey said that timing was everythingMade him want to be everywhereThere's a lot to be said for nowhereThere's no leaving hereAsk- I'm an earHe's disappearedThere's no leaving hereAsk- I'm an earFuck it- we've disappeared

Latest News for: mfcc

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As a member of the board of directors of the MooreFree & Charitable Clinic (MFCC), it has been my privilege to be involved in efforts to fulfill its mission. “With compassion and respect, Moore Free & Charitable Clinic provides health care to the limited-income, uninsured of Moore County.” ... ....

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